Also related is work by
[1]}, who extend grammar variational autoencoders [2]} with hard constraints represented as attribute grammars. In that work, attribute constraints are propagated top-down, and every generated artifact is required to satisfy the top-level constraint. This strategy comes with challenges; as is well-known in the program-synthesis literature [3]}, top-down constraint propagation can lead to unsatisfiability, and require rejection of generated samples, for grammars above a certain level of complexity.
We sidestep this issue by using attribute grammars as a form of weak supervision, rather than as a means to enforce hard constraints.
